The average age of the documents is relatively low, with a value of 2.15 years, indicating that most of the research is recent and reflects current advancements in the field. Additionally, the average number of citations per document is significantly high, with an average of 27.28 citations per document, suggesting that research in this field is widely recognized and cited by other scholars.
Regarding collaboration among authors, it is observed that there is a significant proportion of documents written collaboratively, with an average of 3.81 co-authors per document. Moreover, approximately 36.11% of these collaborations are international, indicating strong global cooperation in research on Industry 4.0 and digital manufacturing.
In terms of document types, the majority of works are reviews, representing 592 documents. Additionally, there is a small number of documents that are reviews combined with book chapters or early access articles.
3.3. Institutional and Geographical Analysis
In what concerns institutions and research centers,
Table 4 contains the main ones in terms of thematic production. The University of Minho (Portugal) leads thematic production with 42 articles, indicating a strong contribution from this institution in the specific analyzed field. It is followed by the University of Johannesburg (South Africa), which ranks second with 31 articles, and the Federal University of Sao Carlos (Brazil) with 25 articles, in third place.
When interpreting the data on thematic production by countries (see
Figure 9), it stands out that the most scientifically productive countries are India, Italy, and China, which account for 11.5%, 9.5%, and 6.7% respectively of the total articles in the sample, followed by countries such as Brazil, the United Kingdom, and the United States, among others. It is noteworthy that the top 10 countries in the ranking generate 58.2% of the total publications. Another notable aspect is the ratio of Single Country Publications (SCP) to Multi-Country Publications (MCP). As seen in the graphic, national production and collaboration prevail; however, countries such as the USA, China, and Australia stand out with more than 40% of their total publications being in collaboration with international authors.
In terms of international collaboration, as can be observed in
Figure 10, there is abundant collaboration among several countries, primarily those mentioned earlier.
Some observations and patterns that can be noted: there are strong connections between some countries, for example, China has strong collaboration with the United States, the United Kingdom, Australia, and Canada. Similarly, India has many collaborations with the United States, the United Kingdom, and Canada. On the other hand, there is a trend towards regional collaboration; several countries in Latin America, such as Brazil, Colombia, and Mexico, have significant collaborations among themselves. The same occurs with several countries in Europe, such as Germany, France, and Italy. Some countries act as “hubs” of global collaboration; the United States and the United Kingdom have significant connections with a wide range of countries in different regions. Identifying these relationships helps identify trends and opportunities for future collaboration.
Using a collaboration network as the analysis source (
Figure 11) and its associated data, it can be observed that India, the United Kingdom, and China are the nodes with the highest intermediary values, suggesting that they act as important bridges between other countries in the collaboration network. In terms of closeness, Norway, Canada, and Croatia have relatively high values, indicating that they are well connected with other nodes in the network. In PageRank terms, India, the United Kingdom, and China also have the highest values, indicating that they are prominent countries in the collaboration network. PageRank is an algorithm used to measure the relative importance of a node in a network, taking into account both the quantity and quality of its connections [
39,
40]. Higher values indicate a greater relative importance of the node in the network.
3.4. Document Analysis
For the analysis of documents, indicators related to citations will be primarily considered.
Table 5 shows the top 25 articles with the highest number of citations from the sample analyzed. The most cited article with 807 citations at the time of this study is presented by [
3], which addresses the impact of the manufacturing industry on economic and social progress, focusing on the Industry 4.0 initiative. It explores how this concept, although not new, has gained attention in both academia and industrial society, highlighting the need to understand its characteristics for a successful transition to digital manufacturing. It also discusses the importance of developing technological infrastructures and management models to facilitate this transformation, while highlighting the lack of evaluation methodologies in the current literature. It is proposed that a comprehensive literature review can help clearly define the design principles of Industry 4.0 and develop implementable scenarios to drive research and practice in this rapidly developing field. Following in the ranking is the document presented by [
41] with 612 citations, on how Industry 4.0 is transforming sustainability. It identifies sustainability functions, highlighting that economic ones are immediate outcomes and pave the way for socio-environmental functions. Its aim is to help stakeholders better understand the opportunities offered by the digital revolution for sustainability and to work together to ensure its effectiveness and fairness. The article by [
42], ranked third in the ranking, addresses the development of Digital Twin technologies in manufacturing, analyzing their connotation, applications, and challenges in Industry 4.0. It summarizes the definition and current state of Digital Twins, reviewing existing technologies and representative applications. Pending research issues in the development of Digital Twins for smart manufacturing are identified. The article by [
43] analyzes how the integration of Industry 4.0 technologies is transforming the healthcare sector towards Health 4.0, describing key technologies, application scenarios, interdisciplinary benefits, and challenges. Meanwhile, ref. [
44] presents a literature review on the evolution of Agriculture 4.0, highlighting the challenge of efficiently organizing complex networks to meet the dynamic needs of the agricultural supply chain. In the article by [
45] the focus is on integrating Industry 4.0 technologies into predictive maintenance in the context of manufacturing, discussing methods, standards, and applications, and highlighting the growing importance of computing in a field traditionally dominated by engineering. In [
7] it examines how Industry 4.0 technologies are applied in the business processes of manufacturing companies, focusing on production scheduling, servitization, circular supply chain management, as well as the increasing combination of IoT, Big Data Analytics, and Cloud. At the same time, it addresses the use of blockchain technology in various Industry 4.0 applications, highlighting the importance of this technology in addressing security and privacy concerns in applications such as smart agriculture and energy management. In the review conducted by [
46] the use of machine learning techniques in additive manufacturing is examined, identifying potential applications in various areas, discussing challenges, and emphasizing the importance of data sharing for the effective adoption of machine learning. Closing the top 10 articles is the one developed by [
47] in which the relationship between Circular Economy and Industry 4.0 is analyzed, highlighting how this combination can directly contribute to various Sustainable Development Goals (SDGs), such as affordable and clean energy, decent work and economic growth, and industry, innovation, and infrastructure.
Table 5.
Most Global Cited Documents.
Table 5.
Most Global Cited Documents.
Ranking | Paper | DOI | TC | TC per Year | Normalized TC |
---|
1 | OZTEMEL E, 2020, J INTELL MANUF [3] | 10.1007/s10845-018-1433-8 | 807 | 161.40 | 10.35 |
2 | GHOBAKHLOO M, 2020, J CLEAN PROD [41] | 10.1016/j.jclepro.2019.119869 | 612 | 122.40 | 7.85 |
3 | LU Y, 2020, ROBOT COMPUT-INTEGR MANUF [42] | 10.1016/j.rcim.2019.101837 | 550 | 110.00 | 7.06 |
4 | ACETO G, 2020, J IND INF INTEGR [43] | 10.1016/j.jii.2020.100129 | 345 | 69.00 | 4.43 |
5 | LEZOCHE M, 2020, COMPUT IND [44] | 10.1016/j.compind.2020.103187 | 297 | 59.40 | 3.81 |
6 | ZONTA T, 2020, COMPUT IND ENG [45] | 10.1016/j.cie.2020.106889 | 267 | 53.40 | 3.43 |
7 | ZHENG T, 2021, INT J PROD RES [7] | 10.1080/00207543.2020.1824085 | 254 | 63.50 | 5.86 |
8 | BODKHE U, 2020, IEEE ACCESS [48] | 10.1109/ACCESS.2020.2988579 | 250 | 50.00 | 3.21 |
9 | GOH GD, 2021, ARTIF INTELL REV [46] | 10.1007/s10462-020-09876-9 | 239 | 59.75 | 5.51 |
10 | DANTAS TET, 2021, SUSTAIN PROD CONSUMP [47] | 10.1016/j.spc.2020.10.005 | 227 | 56.75 | 5.24 |
11 | DOLGUI A, 2020, INT J PROD RES [49] | 10.1080/00207543.2020.1774679 | 218 | 43.60 | 2.80 |
12 | IVANOV D, 2021, INT J PROD RES [50] | 10.1080/00207543.2020.1798035 | 214 | 53.50 | 4.94 |
13 | PERERA S, 2020, J IND INF INTEGR [51] | 10.1016/j.jii.2020.100125 | 210 | 42.00 | 2.69 |
14 | PASCHOU T, 2020, IND MARK MANAGE [52] | 10.1016/j.indmarman.2020.02.012 | 205 | 41.00 | 2.63 |
15 | OSTERRIEDER P, 2020, INT J PROD ECON [53] | 10.1016/j.ijpe.2019.08.011 | 203 | 40.60 | 2.60 |
16 | LENG J, 2021, J MANUF SYST [54] | 10.1016/j.jmsy.2021.05.011 | 200 | 50.00 | 4.61 |
17 | AWAN U, 2021, BUS STRATEG ENVIRON [55] | 10.1002/bse.2731 | 196 | 49.00 | 4.52 |
18 | SIMA V, 2020, SUSTAINABILITY [56] | 10.3390/su12104035 | 186 | 37.20 | 2.39 |
19 | LU Y, 2020, J MANUF SYST [57] | 10.1016/j.jmsy.2020.06.010 | 183 | 36.60 | 2.35 |
20 | SEMERARO C, 2021, COMPUT IND [58] | 10.1016/j.compind.2021.103469 | 179 | 44.75 | 4.13 |
21 | ZHANG C, 2020, J IND INTEGR MANAG [59] | 10.1142/S2424862219500192 | 178 | 35.60 | 2.28 |
22 | BAG S, 2022, INT J ORGAN ANAL [60] | 10.1108/IJOA-04-2020-2120 | 178 | 59.33 | 11.14 |
23 | CINAR ZM, 2020, SUSTAINABILITY [61] | 10.3390/su12198211 | 178 | 35.60 | 2.28 |
24 | BEIER G, 2020, J CLEAN PROD [62] | 10.1016/j.jclepro.2020.120856 | 175 | 35.00 | 2.25 |
25 | SONY M, 2020, BENCHMARKING [63] | 10.1108/BIJ-09-2018-0284 | 173 | 34.60 | 2.22 |
Delving into a content analysis based on available metadata such as keywords and plus keywords yields valuable insights, especially when analyzing their behaviour over temporal periods or subjecting them to certain bibliometric processes.
In the word cloud (
Figure 12), the most frequent keywords from the sample are displayed. Given the terms used to obtain the article sample, the appearance of terms like “Industria 4.0” is not relevant; however, it is pertinent to analyze related topics such as sustainability, artificial intelligence, the Internet of Things, and smart manufacturing. Some of the standout terms include “sustainability”, “artificial intelligence”, “internet of things”, “manufacturing”, and “smart manufacturing”. Terms like “machine learning”, “digital twin”, “circular economy”, “additive manufacturing”, and “blockchain” are also prominent, indicating a focus on advanced technologies and concepts in the context of Industry 4.0. Other relevant terms include “predictive maintenance”, “big data”, “cyber-physical systems”, “supply chain”, “digital transformation”, and “healthcare”, suggesting particular attention to areas such as predictive maintenance, supply chain management, and digital transformation across different sectors, including healthcare. Therefore, it can be affirmed that this analysis reveals a broad and diverse landscape of topics and technologies being researched and discussed in the context of Industry 4.0, ranging from smart manufacturing to sustainability and the application of emerging technologies such as artificial intelligence and the Internet of Things.
Upon analyzing the behavior of the main terms over the 5 years taken as reference, with emphasis on the top 4 terms, we obtained the “Trend topics” graph (
Figure 13), from which the following analysis emerges: “Industry 5.0” appears in 2023, indicating a growing interest in the next phase of industrial development, while “Learning” and “Sustainability” appear in 2022 and 2023, suggesting a shift towards a focus on continuous improvement and sustainability within Industry 4.0. On the other hand, concepts such as “Cyber-Physical Systems” and “Smart Factory” mature, as they are present throughout the period, indicating that they are established areas within Industry 4.0.
From the analysis of the co-occurrence network (
Figure 14) and its accompanying data, emerging trends, mature concepts, and relationships between keywords can be identified. The network is comprised of three main clusters.
Cluster 1 (red): Sustainability is formed by keywords related to sustainability in general, including the term itself, sustainable development, circular economy, sustainable supply chain, and supply chain management. The terms in this cluster have a high betweenness centrality and closeness, indicating their importance in connecting different areas of sustainability research. The terms in this cluster suggest a growing integration of these concepts in Industry 4.0 and their significance for the evolution of manufacturing industries toward Industry 5.0.
Cluster 2 (green): Industry 4.0 is composed of keywords related to Industry 4.0 technologies, including the term itself, Internet of Things (IoT), artificial intelligence (AI), smart manufacturing, and digital twin. A strong connection is observed among the terms in this cluster. These keywords have a high PageRank, indicating their importance within the analyzed articles. Industry 4.0 emerges as a significant term in research on sustainability and digital transformation. The presence of “Covid-19” in this cluster suggests a possible analysis of the pandemic’s impact on smart manufacturing and the need for resilient environments to such phenomena.
Cluster 3 (blue): Machine Learning and Optimization consists of keywords related to machine learning and optimization techniques, including machine learning, additive manufacturing, predictive maintenance, deep learning, and optimization. These keywords have a moderate betweenness centrality, indicating their role in connecting Industry 4.0 with other research areas. Machine learning and optimization appear to be important tools for implementing sustainable Industry 4.0 technologies.
Furthermore, from the analysis of the data associated with the network, it can be identified that Industry 4.0 technologies, especially the Internet of Things and artificial intelligence, are gaining importance in sustainability research. Additionally, there is an emphasis on using machine learning and optimization techniques to implement sustainable technologies in the Industry 4.0 environment.
Continuing with the co-occurrence analysis, significant relationships between keywords from the clusters are identified. As expected, sustainability is related to circular economy, sustainable supply chain, and supply chain management. Industry 4.0 is associated with the Internet of Things, artificial intelligence, smart manufacturing, and digital twin. Machine learning and optimization are linked to additive manufacturing, predictive maintenance, and deep learning.
The previous analysis can be complemented with an assessment of thematic evolution based on keywords. In
Figure 15, keywords such as “security” and “collaboration” show high values in the Weighted Inclusion Index and the Inclusion Index for the period 2022–2024, suggesting a growing emphasis on these aspects of Industry 4.0 implementations. Likewise, the same high values are observed for “manufacturing systems” within the “sustainability” theme in 2022–2024, indicating a growing interest in integrating sustainable practices into manufacturing processes.
As expected, given the keywords used to obtain the reference sample, terms around Industry 4.0 consistently have high values, while the Internet of Things stands out, reaffirming its maturity within the research field.
Other notable relationships are established among keywords such as “artificial intelligence”, “machine learning”, “big data”, and “smart,“ which frequently appear alongside “Industry 4.0”, reaffirming their role as enabling technologies. Meanwhile, “sustainability” appears alongside keywords such as “circular economy”, “digitalization”, and “supply chain management”, suggesting a focus on holistic sustainability practices within Industry 4.0. The data also suggests a growing interest in applying machine learning for various purposes within Industry 4.0, including topics like “predictive maintenance” and “deep learning”. While security remains a concern, as keywords such as “security” and “human-robot collaboration” consistently appear.
The concept of “Industry 5.0” emerges in the data, possibly indicating a new paradigm shift toward the future industrial revolution.
Figure 16 shows the thematic map based on the keywords defined by the authors. Thanks to this map and the analysis of the degree of theme development defined by the density variable and the degree of relevance defined by the centrality variable, driving themes can be identified, which are developed and fundamental for the research field. Niche or delimited topics have higher density but with a lower degree of centrality, so it can be interpreted that they are topics of lesser influence in the research domain despite their high development. Emerging or declining topics are those that present low centrality and density, so their degree of development and relevance is low. This can be analyzed from the temporal variable, meaning that at the time of the research, these are topics that are disappearing or emerging, so they do not have high levels in the analyzed variables. Basic topics, corresponding to the IV quadrant, are those that have not yet been fully developed but are relevant to research.
From the sample obtained and as can be seen in
Figure 16, there are five thematic clusters. The topics of the Internet of Things and artificial intelligence associated with manufacturing (red cluster) are the most relevant and developed. At the same time, topics associated with circular economy and sustainability (blue cluster) have achieved considerable relevance above the average and possess a degree of development around the average, indicating that these themes associated with manufacturing processes have been gaining relevance in the last 5 years. Topics associated with measurements and uncertainty in manufacturing processes have experienced notable development but are well-divided or isolated with little impact on the analyzed research domain. Topics associated with security, human factors, and human–robot collaboration, although they have relevance around the average, do not reach development close to the average, so it can be interpreted that not all research opportunities have been exhausted in this regard. Finally, two thematic groups associated with applications and metal additive manufacturing are identified, which have both low relevance and low development degrees.
A Multiple Correspondence Analysis (MCA) is a multivariate statistical technique used to analyze relationships between categorical variables. In bibliometrics, this technique can be used to analyze relationships between keywords, identifying groups of related keywords that are frequently used together. Additionally, it helps identify relationships between keywords, such as synonyms, antonyms, or other related terms. MCA can help identify keywords that discriminate between different groups of articles.
The most significant cluster for this analysis according to
Figure 17 is the red cluster, which groups terms related to research fields such as “sustainable development”, “sustainability”, “supply chain management”, “manufacturing”, “management”, “digitalization”, “digital twin”, and “digital transformation”. This cluster addresses topics related to sustainability, supply chain management, manufacturing, digital business management, and digital transformation, all of which are relevant aspects in manufacturing processes and Industry 4.0. Cluster 2 focuses on aspects related to security, connectivity, data protection, and information technology in the context of advanced manufacturing and Industry 4.0. These topics are critical for ensuring the integrity and efficiency of industrial processes in an increasingly digitized and connected environment. Cluster 3 is related to artificial intelligence, machine learning, optimization, and predictive maintenance in the industry. These terms suggest a focus on advanced technologies to improve efficiency, productivity, and quality in industrial processes, which is consistent with the trend toward Industry 4.0 and the adoption of digital technologies in manufacturing.